On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model

Abstract

The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient. To overcome the multicollinearity problem, a new two-parameter estimator, a biased two-parameter (BTP), is proposed as an alternative to the OLS. Theoretical comparisons and simulation studies were carried out. The theoretical comparison and simulation studies show that the proposed estimator dominated some existing estimators using the mean square error (MSE) criterion. Furthermore, the real-life data bolster both the hypothetical and simulation results. The proposed estimator is preferred to OLS and other existing estimators when multicollinearity is present in the model.

A New Biased Two-Parameter Estimator in Linear Regression Model

“…Several researchers have proposed alternative estimators to the OLS to overcome these effects of multicollinearity. The authors include Hoerl and Kennard [2], Liu [3], Stein [4], Swindel [5], Ahmad and Aslam [6], Lukman et al [7], Kibria and Lukman [8] , Efron et al [9], Draper and Smith [10], Mansson et al, [11], Dempster et al [12], Akdeniz and Roozbeh [13], Muniz et al, [14], Arashi and Valizadeh [15], Ayinde et al, [16], Yang and Chang [17], Owolabi et al, [18,19], Oladapo et al, [20], and Idowu et al [21] This paper aims to introduce a new class of two-parameter estimator to estimate regression parameters when there is a problem of multicollinearity and compare the performance of the new estimator with the existing estimators.…”